Systems and methods for facilitating searching, labeling, and/or filtering of digital media items

ABSTRACT

In certain embodiments, media item search and machine learning system training may be facilitated. In some embodiments, a first set of media items may be obtained (based on performance of a query) and presented on a user interface. A user selection of a media item of the first set may be obtained, and the query may be updated based on the user-selected media item. A second set of media items may be obtained based on performance of the updated query, and media items of the second set may be assigned to a group based on their similarities with one another. A predicted name for the group may be determined via a machine learning system and presented on the user interface. A user-indicated update to the predicted name for the group may be obtained and provided to the machine learning system to train the machine learning system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/792,478, filed Jul. 6, 2015, which claims the benefit of U.S.Provisional Application No. 62/021,572 filed on Jul. 7, 2014, thecontent of each of which is hereby incorporated herein by reference inits entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems and methods for facilitatingsearching, labeling, and/or filtering of digital media items, such aswithin one or more digital media item repositories

BACKGROUND

Digital media item repositories are common across many businesses and/orpersonal collections. A digital media item may include one or more of adigital image, a video, an audio file, other non-text-based digitalitems, digital items that include text, and/or other digital mediaitems. User-contributed collections may be used for some internetbusinesses. Users may perform searches to find, research, and/orotherwise browse through such collections Keywords and/or textualmetadata may be associated with individual digital media items. Thekeywords and/or other textual metadata may be indexed for searchingpurposes. In some contexts, them may be little to no associated keywordsor textual metadata associated with individual digital media items. Itmay be difficult and/or tedious for a user to describe in words adigital media item he or she seeks.

SUMMARY

One aspect of the disclosure relates to a system configured forfacilitating searching, labeling, and/or filtering of digital mediaitems. A digital media item may include one or more of a digital image,a video, an audio file, other non-text-based digital items, digitalitems that include text, and/or other digital media items. One or morecomponents of the system may be configured to obtain one or more queriesfrom one or more users and perform a search for digital media items thatsatisfy the queries. Results from the search may be presented to theusers. A user may provide selection of one or more displayed digitalmedia items. A user selection may provide an image exemplar that may beused to update the user-provided queries. Context information may beobtained for the user-selected digital media items. The contextinformation may be used to generate an updated quay and perform furthersearches for digital media items based on the updated query. As such, insome implementations, a repository of digital media items may be queriedboth with the original text query supplied by a user and contextinformation of user-selected digital media items returned from theoriginal text query. Thus, a user may be able to refine an initial textquay without having to know additional search terms.

In some implementations, individual digital media items and/or groups ofdigital media items may be associated with context information and/orother information. Context information may include one or more of acategory of a digital media item, a geolocation, a timestamp, a price, asemantic description, a content description, a rating, and/or otherinformation associated with a given digital media item that may providecontext for a digital media item. Context information associated withindividual ones of the digital media items may be referred to as a“label,” “tag,” and/or other terms. An association of contextinformation to individual ones of the digital media items may bereferred to as “labeling,” “tagging,” and/or other terms Contextinformation associated with a digital media item may be stored asmetadata of the digital media item and/or associated with the digitalmedia item in other ways. In some implementations, context informationmay be generated for a given digital media item based on one or more ofa prediction of the context information, user-provided information,and/or other techniques.

In some implementations, the system may comprise one or more physicalprocessors configured by machine-readable instructions. Executing themachine-readable instructions may cause the one or more physicalprocessors to facilitate searching for digital media items within one ormore digital media item repositories. The machine-readable instructionsmay include one or more of a query component, a search component, aresults component, a selection component, a label component, and/orother components.

In some implementations, the query component may be configured tofacilitate one or more of obtaining queries for digital media items,updating queries, and/or performing one or more other operations. Insome implementations, queries may be obtained from computing platformsassociated with users of the system and/or other sources. By way ofnon-limiting example, users may submit queries via one or more inputdevices of a computing platform. The query component may be configuredto obtain a first query for digital media items and/or other queries.

The query component may be configured to update the obtained queriesbased on a user selection of one or more digital media items that may bepresented to the user. Updating an obtained query may include one ormore of replacing the obtained query with a new query, adding one ormore terms to the obtained query, removing one or more terms from theobtained query, and/or other types of updates.

In some implementations, the search component may be configured toperform searches within one or more digital media item repositories forone or more digital media items that satisfy the obtained queries and/orupdated queries. By way of non-limiting example, the search componentmay be configured to perform a first search and/or other searches withina first digital media item repository and/or other digital media itemrepositories for one or more digital media items that satisfy theobtained first query and/or other queries.

The results component may be configured to effectuate presentation ofsets of digital media items that satisfy the obtained queries and/orupdated queries via computing platforms associated with users. By way ofnon-limiting example, responsive to the first search returning a firstset and/or other sets of digital media items that satisfy the firstquery, the first set and/or other sets of digital media items may bepresented on a first computing platform.

The selection component may be configured to obtain a user selection ofone or more digital media items in the presented sets of digital mediaitems. By way of non-limiting example, the selection component may beconfigured to receive a first user selection of a first digital mediaitem included in the first set of digital media items presented on thefirst computing platform. The query component may be configured toupdate the first query based on the first user selection of the firstdigital media item. The search component may be configured to perform asecond search and/or other searches within the first digital media itemrepository and/or other digital media item repositories for one or moredigital media items that satisfy the updated first query.

In some implementations, the label component may be configured to obtainuser selection from the users of a modification to the contextinformation that is presented with the digital media items of a givengroup, such that a first modification to the presented first contextinformation is entered and/or selected by the first user; and/or updatecontext information that is associated with individual digital mediaitems of a given group based on the modifications to the contextinformation that is presented with the digital media items of the givengroup, such that the first context information associated with thesecond digital media item is updated based on the first modification tothe first context information presented with the digital media items ofthe first group.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular forms of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured for facilitating searching,labeling, and/or filtering of digital media items, in accordance withone or more implementations.

FIG. 2 illustrates an exemplary server used in the system of FIG. 1, inaccordance with one or more implementations.

FIG. 3 illustrates a method for facilitating searching, labeling, and/orfiltering of digital media items, in accordance with one or moreimplementations.

FIG. 4 illustrates an exemplary implementation of a search component ofmachine-readable instructions in the system of FIG. 1.

FIG. 5 illustrates a context information prediction and indexing system,in accordance with one or more implementations.

FIG. 6 illustrates an exemplary user interface, in accordance with oneor more implementations.

FIG. 7 illustrates an exemplary display view for presenting digitalmedia items in a user interface.

FIG. 8 illustrates another exemplary display view for presenting digitalmedia items in a user interface.

FIG. 9 illustrates yet another exemplary display view for presentingdigital media items in a user interface.

FIG. 10 illustrates yet another exemplary display view for presentingdigital media items in a user interface

FIG. 11 illustrates yet another exemplary display view for presentingdigital media items in a user interface.

FIG. 12 illustrates an exemplary implementation of a user interface.

FIG. 13 illustrates another exemplary implementation of a user interface

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 configured for facilitating searching,labeling, and/or filtering of digital media items, in accordance withone or more implementations. A given digital media item may include oneor more of an image, a video, an audio file, financial data, time seriesinformation, pricing data, binary data, and/or other digital mediaitems.

In some implementations, individual digital media items and/or groups ofdigital media items may be associated with context information and/orother information. Context information may include one or more of acategory of a digital media item, a geolocation, a timestamp, a price, asemantic description, a representation of the digital media item invisual-semantic embedding space, a content description, a rating, a userreview, a device footprint, and/or other information associated with agiven digital media item that may provide context and/or otherinformation for a digital media item. Context information associatedwith individual ones of the digital media items may be referred to as a“label,” “tag,” “class,” “category,” “concept,” “metadata,” and/or otherterms. An association of context information to individual ones of thedigital media items may be referred to as “labeling,” “tagging,”“classifying,” “categorizing,” “recognizing,” and/or other terms.Context information associated with a digital media item may be storedas metadata of the digital media item and/or associated with the digitalmedia item in other ways.

In some implementations, context information may be generated for agiven digital media item based on one or more of a prediction of thecontext information, user-provided information, and/or other techniques.By way of non-limiting illustration in FIG. 5, an exemplary contextinformation determination and/or indexing system 500 is shown. System500 may include one or more physical processors 504 configured bymachine-readable instructions (not shown in FIG. 5), indexes 506, and/orother components. Executing the machine-readable instructions may causeone or mon physical processors 504 to facilitate one or more ofpredicting context information for individual digital media items,indexing the context information within one or more indexes 506 tofacilitate searching of the digital media items, and/or otheroperations. In some implementations, indexes 506 may comprise one ormore rational databases of information that provide a basis againstwhich searches for digital media items may be performed in order toretrieve a desired digital media item. The machine-readable instructionsof one or more physical processors 504 may include one or more of a tagcomponent 508, an embeddings component 510, a quality component 512, afiltering component 514, and/or other components.

In some implementations, the tag component 508 may be configured toobtain digital media items in the repository 502. Tag component 508 maybe configured to predict text-based context information to associatewith individual ones of the digital media items. By way of non-limitingexample, the tag component 508 may comprise a machine learning systemthat may have been previously trained to predict context information. Insome implementations, predicted text-based context information may bestored in a text index 305. Text index 305 may comprise, for example, adata structure stored within electronic storage (not shown in FIG. 5) ofsystem 500 and/or other components.

In some implementations, embeddings component 510 may be configured todetermine embeddings for individual ones of the digital media items ofdigital media item repository 502. In some implementations, embeddingsmay be associated with a similarity (or dissimilarity) between digitalmedia items, and/or other information. By way of non-limiting example,similarity between two or more digital media items may be determinedbased on a “distance” between two points that represent individualdigital media items in an “embedding” space and/or other representativespace. By way of non-limiting example, representative points may beportrayed as “closer” together in the space if corresponding digitalmedia items are relatively more similar. By way of non-limiting example,a representative space may comprise a visual-semantic embedding spaceand/or other representative space. A visual-semantic representativespace may facilitate a mapping from digital media items to points in therepresentative space. By way of non-limiting example, a metric space,token-based feature space, hashing space, mapping space, semantic space,and/or type of visual-semantic embedding space may be employed.

In some implementations, distances between representative points may becomputed to determine similarities of the digital media items associatedwith the points. A compact description of individual digital media itemsmay be computed to map semantically similar digital media items tonearby points in the space. By way of non-limiting example, a compactdescription may be determined by a machine learning system that may havebeen trained for the desired tasks. By way of non-limiting illustration,digital media items comprising images of “dogs” may be represented asbeing closer together with other images of “dogs” within the space;digital media items comprising images of “dogs of the same breed” may beeven closer within the space; digital media items comprising images of“dogs” may be represented as being closer to images of “cats” than toimages of “cars”; and/or other representations may be made within avisual-semantic embedding space. It is noted that the above examples ofdigital media items comprising images of “dogs,” “cats”, and/or “cars”is provided for illustrative purposes only and is not to be consideredlimiting. For example, in some implementations, embeddings and/orsimilarity may be determined for other types and/or content of digitalmedia items.

In some implementations, one or more embeddings determined by embeddingscomponent 510 may be stored in an embedding index 306. Embedding index306 may comprise, for example, a data structure stored within electronicstorage (not shown in FIG. 5) of system 500 and/or other components.Embedding index 306 may be configured to provide one or more of ahigh-dimensional approximate near-neighbor vector indexing system, tokenbased lookup, hash tables, and/or other indexing systems.

In some implementations, quality component 512 may be configured todetermine values for one or more quality parameters for one or moredigital media items. The determined values may provide aquery-independent subjective quality measure for individual ones of thedigital media items. By way of non-limiting example, a quality measuremay facilitate distinguishing a relatively high-quality digital mediaitem from a relatively low-quality digital media item. Values for aquality parameter may be determined based on one or more of a visualquality, audio quality, interaction rank, and/or other quality measure.By way of non-limiting example, a visual quality may be determined basedon one or more visual aspects of a given digital media item. A visualaspect may correspond to one or more of an image artifact, distortion,blurriness, lighting, composition, and/or other visual aspects of agiven digital media item. An audio quality may be determined based onone or more of an audible distortion being present in an audio track ofa given digital media item, the encoding quality, and/or otheraudio-related qualities. An interaction rank may be associated with oneor more of a “click log” associated with the digital media item (e.g.,an amount of human interactions with the given digital media item,and/or other information), an amount of social media “likes,”popularity, comments, shares, and/or other social media interactionsand/or metrics; and/or other information.

In some implementations, the filter component 514 may be configured tofilter digital media items for unwanted content. Unwanted content mayinclude one or more of content that may be offensive, inappropriate forchildren, copyright-infringing, and/or other types of content. Filtercomponent 514 may be configured to associate filter information withindividual digital media items. Filter information may convey whetherthe digital media item is “unwanted content,” “allowable content,”and/or associated with other types of content. Determined values ofquality parameters and/or filtering information may be stored inquality/filter index 307 and/or other storage location.

It is noted that the system 500 of FIG. 5 is provided for illustrativepurposes only and is not to be considered limiting. By way ofnon-limiting example, other exemplary systems and/or methods forassociating context information with a digital media item may include,but are not limited to, those described in one or both of U.S.Provisional Application No. 62/106,648, titled “User Interface forContext Labeling of Multimedia Items,” and/or U.S. ProvisionalApplication No. 62/084,506, titled “User Interface for Labeling,Browsing, And Searching Semantic Labels Within Video,” each of which isincorporated herein by reference in its entirety.

Returning to FIG. 1, in some implementations, the system 100 maycomprise one or more servers 102, one or more networks 120, one or moreexternal resources 122, one or more digital media item repositories 124,one or more computing platforms 126, and/or other components. Server 102may include one or more physical processors 104 configured bymachine-readable instructions 106. Executing machine-readableinstructions 106 may cause one or more physical processors 104 tofacilitate searching for digital media items within one or more digitalmedia item repositories. Machine-readable instructions 106 may includeone or more of a query component 108, a search component 110, a resultscomponent 112, a selection component 114, a label component 116, and/orother components.

In some implementations, server 102 may be configured to provide remotehosting of one or more features and/or functions of machine-readableinstructions 106 to one or more computing platforms 126 that may beremotely located from server 102. In some implementations, one or morefeatures and/or functions of server 102 may be attributed to localfeatures and/or functions of one or more computing platforms 126. By wayof non-limiting example, individual ones of the computing platforms 126may include machine-readable instructions comprising the same or similarcomponents as machine-readable instructions 106 of server 102. Computingplatforms 126 may be configured to locally execute the one or morecomponents that may be the same or similar to machine-readableinstructions 106.

Computing platforms 126 may include one or more of a cellular telephone,a smartphone, a laptop, a tablet computer, a desktop computer, atelevision set-top box, smart TV, a gaming console, and/or othercomputing platforms. A given computing platform 126 may include a localdigital media item repository 128 and/or other components.

In some implementations, digital media items may be stored in one ormore of electronic storage 118 of server 102, one or more externalresources 122, a remote digital media item repository 124 accessible byserver 102, and/or computing platforms 126 via network 120, a digitalmedia item repository 128 local to a given computing platform 126,and/or other storage locations. By way of non-limiting example, digitalmedia item repository 124 may comprise one or more of a third-partyelectronic storage location for digital media items, and/or other typeof remote digital media item repository configured to store digitalmedia items. In some implementations, digital media item repository 128of a given computing platform 126 may be incorporated as part of localelectronic storage of given computing platform 126.

In some implementations, query component 108 may be configured tofacilitate one or more of obtaining queries for digital media items,updating queries, and/or performing one or more other operations. Insome implementations& queries may be obtained from one or more computingplatforms 126 associated with one or more users of system 100 and/orfrom other sources. By way of non-limiting example, a given user maysubmit one or more queries via a given computing platform 126. Computingplatforms 126 may include one or more input devices configured tofacilitate user input of a query and/or other information. An inputdevice may include one or more of a keyboard, a touchscreen, amicrophone, a camera, and/or other input devices.

One or more computing platforms 126 may be configured to effectuatepresentation of one or more user interfaces. A given user interface mayinclude one or more user interface elements. A user interface elementmay include one or more of an input element, a navigation element, adisplay element, and/or other elements. An input element may beconfigured to receive entry and/or selection of user input via one ormore input devices of computing platform 126. An input element maycomprise one or more of a text-input field, a drop-down list, a listbox, a checkbox, a search field, a button, and/or other input elements.A navigation element may be configured to facilitate navigating betweendifferent pages, views, and/or other parts of a user interface. Anavigation element may include one or more of a breadcrumb, a slider,pagination, a page, a tab, an icon, an image carousel, and/or othernavigation elements. A display element may be configured to presentinformation to a user via the user interface. A display element mayinclude one or more of a window, a results display area, and/or othercomponents.

A given user interface element may be configured to receive user inputof one or more queries and/or other information. A query may include oneor more of a text-based query, an image-based query, and/or other typesof queries. An image-based query may include entry and/or selection ofan image exemplar and/or other information. By way of non-limitingillustration in FIG. 6, an exemplary user interface 600 is shown. Theuser interface 600 may include one or more user interface elements. Theone or more user interface elements may include one or more of a firstinput element 602, a first display element 604, a second input element606, and/or other user interface elements. First input element 602and/or other input elements may include a text input field and/or othertype of input element. First input element 602 and/or other inputelements may be configured to receive user input of one or moretext-based and/or digital-media-item-based queries. By way ofnon-limiting example, a user may type a text-based query into firstinput element 602 In some implementations, a user may enter a digitalmedia item as a query by one or more of uploading first input element602, performing a “drag and drop” input into the first input element602, and/or by other techniques.

By way of non-limiting illustration in FIG. 2, query component 108 maybe configured to obtain a first query 202 The first query 202 may beassociated with input by a first user via a first computing platformassociated with the first user (not shown in FIG. 2).

Returning to FIG. 1, search component 110 may be configured to performone or more searches within one or more digital media item repositoriesfor one or more digital media items that satisfy one or more obtainedqueries and/or other queries (e.g., updated queries, described herein).In some implementations, searches may be performed by querying one ormore indexes associated with a digital media item repository (e.g.,indexes 506 in FIG. 5) and/or other by other techniques.

FIG. 4 illustrates an exemplary implementation of search component 110being configured to perform one or more searches within one or moredigital media item repositories for one or more digital media items thatsatisfy one or more queries. In some implementations, performing a givensearch may include one or more of retrieving one or more digital mediaitems from one or more digital media item repositories that satisfy oneor more queries, determining similarity between individual digital mediaitems, grouping one or more digital media items together based onsimilarity, and/or other operations.

In some implementations, search component 110 may comprise one or moresubcomponents. The subcomponents may comprise one or more of a retrievalsubcomponent 402, a scoring subcomponent 404, a grouping subcomponent406, and/or other components. (4N) In some implementations, retrievalsubcomponent 402 may be configured to retrieve one or more digital mediaitems and/or other information. In some implementations, retrieving oneor more digital media items may comprise utilizing one or more of atext-based retrieval technique (e.g., for text-based queries), animage-based retrieval technique (e.g., for image-based queries), and/orother retrieval techniques to retrieve one or more digital media itemsthat satisfy one or more queries. By way of non-limiting example, insome implementations, a text query may be utilized to retrieve one ormore sets of digital media items that satisfy terms of the text query.

In some implementations, for an image-based query, the search component110 may be configured to obtain context information of a user-providedimage exemplar (e.g., if such information is present with the image)and/or generate predicted context information (e.g., text, embeddings,and/or other information) for the image exemplar. Text-based contextinformation of the image-based query may be converted into terms of atext query. The text query may be used to retrieve one or more sets ofdigital media items based on the one or more digital media itemssatisfying the terms of the text query. In some implementations,embeddings-type context information may be used to query a near-neighborindexing system to retrieve one or more sets of digital media items(e.g., using the same or similar components as system 500 shown in FIG.5 and described herein).

In some implementations, scoring subcomponent 404 may be configured todetermine a similarly between individual digital media items. In someimplementations, similarly may correspond to one or both of semanticsimilarity, visual similarity, and/or other types of similarity.Similarity may be based on determining one or more similarity scoresbetween individual digital media items and/or by other techniques.Similarity scores may be determined based on one or more of a comparisonof context information of retrieved digital media items and/or othertechniques.

In some implementations, a similarity score may be a numeric scoreand/or other representation of a score that may represent a degree,percentage, and/or other measure of similarity between at least twodigital media items. In some implementations, a similarly score may bedetermined based on an amount and/or fraction of matching contextinformation and/or other matching information associated with thedigital media items, a qualitative comparison of individual features ofdigital media items, and/or other techniques. In some implementations,similarity scores may be normalized.

In some implementations, the grouping subcomponent 406 may be configuredto assign one or more digital media items to a given group based onsimilarity scores and/or other information. Grouping one or more digitalmedia items may comprise one or more of determining duplicates and/ornear-duplicates 408 of digital media items based on similarly scores,grouping 410 other non-duplicate digital media items based on similarityscores, determining a name 412 of a given group, and/or otheroperations.

In some implementations, duplicates and/or near-duplicates may bedetermined based on a similarity score threshold that may convey that atleast two digital media item may be duplicates and/or near-duplicates.By way of non-limiting example, digital media items that may exhibitsimilarity scores above a given threshold may be considered duplicatesand/or near-duplicates of each other. By way of non-limiting example,two or more digital media item that may exhibit a similarity score of95%, and/or other percentage and/or degree of similarly, may beconsidered duplicates and/or near-duplicates.

Assigning digital media items to groups may be based on assigningsemantically and/or visually similar digital media items to commongroups. By way of non-limiting example, similarity scores of digitalmedia items with a given value and/or range of values may convey thatthe digital media items may be semantically and/or visually similar.Digital media items with similarity scores of the given value and/orrange may be assigned to the same group, while digital media itemshaving similarity scores of another given value and/or range of valuesmay be assigned to different groups. By way of non-limiting example,digital media items determined to be in the range of 35-95% similar maybe assigned to a given group, while digital media items determined to bebelow 55% similar may be assigned to different groups.

In some implementations, within a visual-semantic embedding space, forindividual digital media items of a given group, a centrality score mayfurther be determined. A centrality score may be a function of adistance of a digital media item from a group center represented in anembedding space. By way of non-limiting example, a group center maycomprise a central digital media item that may be most representative ofthe group within the embedding space.

In some implementations, individual groups may be assigned predictednames. Predicted names may be determined by one or more of an aggregateof context information of the digital media items within the group(e.g., predicted and/or previously associated context information thatmay be commonly shared between the digital media items), a weighting byrepresentativeness (e.g., distance from the group center in theembedding space), and/or other information.

By way of non-limiting illustration in FIG. 2, search component 110 maybe configured to perform a first search 208, second search 212, and/orother searches 216 within one or more digital media item repositories.The first search 208 may be associated with the first query 202. Thefirst search may be performed within a first digital media itemrepository (not shown in FIG. 2) for one or mom digital media items thatsatisfy the first query 202.

In some implementations, performing the first search 208 may compriseone or more of retrieving a first set 210 of digital media items fromthe first digital media item repository that satisfy the first query202, assigning digital media items included in the first set 210 ofdigital media items into groups, and/or other operations. In someimplementations, assigning digital media items of the first set 210 intogroups may comprise one or more of obtaining context informationassociated with individual ones of the digital media items included inthe first set 210, assigning digital media items included in the firstset 210 into individual groups based on common context informationshared between digital media items, and/or other operations. By way ofnon-limiting example, the first set 210 may include a first digitalmedia item (not shown in FIG. 2). The first digital media item may beassociated with first context information. The first digital media itemmay be assigned to a first group based on the first context informationbeing common between the first digital media item and one or more otherdigital media items of the first group.

Returning to FIG. 1, the results component 112 may be configured tofacilitate one or more of obtaining sets of digital media items and/orrepresentations (e.g. copies) of digital media items that satisfy theobtained queries, effectuating presentation of sets of digital mediaitems on computing platforms 126 associated with users, and/or otheroperations. In some implementations, the results component 112 may beconfigured to obtain digital media items and/or representations ofdigital media items from one or both of search component 110 and/ordirectly from one or more digital media item repositories.

Effectuating presentation of sets of digital media items that satisfythe obtained queries may comprise communicating views of the digitalmedia items to user interfaces presented on displays of computingplatforms 126. In some implementations, digital media items within agiven set of digital media items may be presented based on theirrespective assigned groups (e.g., as illustrated in FIGS. 7-11 anddescribed herein). In some implementations, predicted group names may bepresented along with the digital media items.

By way of non-limiting illustration in FIG. 6, the first display element604 of user interface 600 may be configured to present views of digitalmedia items of a set of digital media items. The first display element604 may display one or more groups of digital media items included in aset of digital media items returned from a query search. The one or moregroups may include a first group 608, a second group 612, and/or othergroups. A predicted first name 610 of the first group 608 may bepresented along with the display of the first group 608. It is notedthat individual ones of the groups of digital media items may bedisplayed in accordance with one or more display views (shown in FIGS.7-11 and described herein).

FIGS. 7-11 illustrate various display views for presenting groups ofdigital media items.

FIG. 7 illustrates a first display view comprising a grid view. The gridview may comprise one or more digital media items of substantially thesame size being disposed and arranged in a grid as shown in the figure.

FIG. 8 illustrates a second display view comprising a multiscale gridview. The multiscale grid view may comprise one or more digital mediaitems wherein at least some of the digital media items may be portrayedrelatively larger than other ones of the digital media items. One ormore digital media items that may be larger than other ones of thedigital media items may represent digital media items that may be mostrepresentative of the group with which they are associated (e.g.,corresponding to a most central representative point in avisual-semantic embedding space).

FIG. 9 illustrates a third display view comprising a flower petal gridview. The flower petal view may comprise one or more digital media itemswherein at least some of the digital media items may be portrayedrelatively larger than other ones of the digital media items One or moredigital media items that may be larger than other ones of the digitalmedia items may represent digital media items that may be mostrepresentative of the group with which they are associated (e.g.,corresponding to a most central representative point in avisual-semantic embedding space).

FIG. 10 illustrates a fourth display view comprising a scatter view. Thescatter view may comprise one or more digital media items wherein atleast some of the digital media items may be portrayed relatively largerthan other ones of the digital media items. The digital media items maybe scattered throughout a display element of a user interface. One ormore digital media items that may be larger than other ones of thedigital media items may represent digital media items that may be mostrepresentative of the group with which they are associated (e.g.,corresponding to a most central representative point in avisual-semantic embedding space).

FIG. 11 illustrates a fifth display view comprising a stack-and-hoverview. The stack-and-hover view may comprise one or more digital mediaitems overlaid in stack arrangement (e.g., left-hand side of thefigure). When a user controls a cursor over the stack arrangement (e.g.,hovers the cursor over the stack), the stack may change to one or moreother display views (e.g, right-hand side of the figure). The one ormore other display views may comprise one or more of a grid view, amultiscale grid view, a flower petal view, a scatter view, astack-and-hover view, a stacked view, a single media item view, and/orother display view.

By way of non-limiting illustration in FIG. 2, the results component 112may be configured to effectuate a first presentation 218 of the firstset 210 of digital media items on a first computing platform, and/orother presentations 220 of other sets of digital media items on one ormore other computing platforms. In some implementations, the resultscomponent 112 may be configured to effectuate presentation of individualones of the digital media items of the first set 210 and/or other setsof digital media items based on their respective assigned groups. By wayof non-limiting example, presenting individual ones of the digital mediaitems of a set of digital media items based on their respective assignedgroups ay comprise presenting digital media items of a given group usingone or more of a grid view, multiscale grid view, flower petal view,scatter view, stack-and-hover view, and/or other display views

Returning to FIG. 1, the selection component 114 may be configured toobtain user selection of one or more digital media items in thepresented sets of digital media items, and/or other information. In someimplementations, user selection of one or more digital media items mayfacilitate updating one or more user-provided queries (e.g., via querycomponent 108). User selection may be facilitated by user input via oneor more input devices of a computing platform 126 associated with theuser.

By way of non-limiting illustration in FIG. 2, selection component 114may be configured to obtain first user selection 222 of a first digitalmedia item included in first set 210 of digital media items by the firstuser via the first computing platform, and/or other user selections 224

Returning to FIG. 1, query component 108 may be configured to updateobtained queries based on user selections of one or more digital mediaitems presented to the users. In some implementations, updating a givenquery may comprise one or more of adding terms to the query, removingterms from the query, replacing the query with a new query, and/or othertechniques.

In some implementations, updating a given query by adding terms to thequay may comprise one or more of obtaining context informationassociated with individual ones of the user-selected one or more digitalmedia items, determining text-based terms from the context information,adding the determined text-based terms to the query, and/or otheroperations. By way of non-limiting example, updating a first query basedon a user selection of a first digital media item may comprise one ormore of obtaining first context information of the first digital mediaitem, determining a first textual term from the first contextinformation, adding the first textual term to text of the first query,and/or other operations.

In some implementations, updating a given query by replacing the querywith a new query may comprise one or more of obtaining contextinformation associated with individual ones of the one or moreuser-selected digital media items, determining text-based terms from thecontext information, providing the determined text-based terms as a newquery, and/or other operations. By way of non-limiting example, updatinga first query based on a user selection of a first digital media itemmay comprise one or more of obtaining first context information of thefirst digital media item, determining a first textual term from thefirst context information, providing the first textual term as a term ofa second query, and/or other operations.

Byway of non-limiting illustration in FIG. 2, query component 108 may beconfigured to update 204 first query 202 and/or other queries 207Updating 204 the first query 202 may provide an updated first query 206.The update 204 may be based on first user selection 222 of the firstdigital media item included in first set 210 of digital media itemspresented on the first computing platform.

In some implementations, updating 204 first query 202 may comprise oneor more of obtaining first context information associated with firstuser selection 222 of the first digital media item, updating first quay202 by adding text-based terms of the first context information in firstquay 202, updating first query 202 by replacing first query 202 with asecond query that comprises the textual terms of first contextinformation, and/or other operations.

Returning to IG. 1, search component 110 may be configured to performone or more searches within one or more digital media item repositoriesfor one or more digital media items that satisfy one or more updatedqueries. By way of non-limiting example, search component 110 may beconfigured to retrieve one or more other sets of digital media itemsthat satisfy a given updated query.

By way of non-limiting illustration in FIG. 2, the search component 110may be configured to perform a second search 212 within the firstdigital media item repository and/or other digital media itemrepositories for one or more digital media items that satisfy updatedfirst query 206. By way of non-limiting example, search component 110may be configured to retrieve a second set 214 of digital media itemsbased on the second search 212. The second set 214 may comprise one ormore digital media items not included in the first set 210.

Returning to FIG. 1, results component 112 may be configured tofacilitate one or more of obtaining sets of digital media items and/orrepresentations (e.g., copies) of digital media items that satisfy theupdated queries, effectuating presentation of sets of digital mediaitems that satisfy the updated queries on computing platforms 126associated with users, and/or other operations.

By way of non-limiting illustration in FIG. 2, results component 112 maybe configured to effectuate a second presentation 219 of second set 214of digital media items and/or other digital media items on the firstcomputing platform. In some implementations, effectuating secondpresentation 219 may further comprise effectuating presentation ofcontext information that may be commonly shared between digital mediaitems of a given group of digital media items of second set 214 (e.g.,an assigned group name) with the presentation of the digital media itemsof the given group.

Returning to FIG. 1, the label component may be configured to facilitateone or more of obtaining user entry of context information for one ormore digital media items and/or groups of digital media items, updatingcurrent context information of the one or more digital media itemsand/or groups of digital media items based on the user-entered contextinformation, and/or other operations. In some implementations, updatesto context information may include one or more of adding information tocurrently present context information, removing information from thecurrently present context information, replacing information in thecurrently present context information, and/or other modificationtechniques. In some implementations, updating context information mayfacilitate allowing the updated context information to be used forindexing context information of a given digital media item such that theupdated context information may now be used as a search term.

By way of non-limiting illustration in FIG. 6, second input element 606of user interface 600 may comprise a text input field and/or other inputelement. Second input element 606 may be associated with a displayedassigned name of the second group 612 of digital media items. The secondinput element 606 may be configured to receive user entry of contextinformation. The user entry may facilitate one or more of changing theassigned name given to the second group 612, adding the user-enteredcontext information to the existing context information of the digitalmedia items included in the second group 612, replacing existing contextinformation of the digital media items included in the second group 612with the user-entered context information, and/or other operations. Insome implementations, updating context information may be referred to as“training” one or more components of the system 100 to “learn” theuser-entered context information.

By way of non-limiting illustration in FIG. 2, the label component 116may be configured to obtain a first user entry 226 and/or other userentries 230 of context information. By way of non-limiting example, thefirst user entry 226 may be provided in association with a presentedassigned name of a group of digital media items presented on a computingplatform associated with a user performing a search and/or provided viaone or more other text input elements of a user interface. The labelcomponent 116 may be configured to provide a first update 228 and/orother updates 232 of first context information associated with a firstdigital media item and/or other digital media items based on the firstuser entry 226 and/or other user entries 230.

FIG. 12 illustrates an exemplary implementation of a user interface 1200configured for presentation on a display of a computing platformassociated with a user of a system configured to facilitate searchingfor digital media items within one or more digital media itemrepositories (e.g., system 100 in FIG. 1, described herein). The userinterface 1200 may include one or more user interface elements. The oneor more user interface elements may comprise one or more of a firstinput element 1202, a second input element 1204, a first display element1206, and/or other user interface elements. The first input element 1202may be configured to receive user input of one or more of text-basedqueries, image-based queries, and/or other types of queries.

By way of non-limiting example, a first query comprising the term “cat”is illustrated. The first display element 1206 may be configured toeffectuate presentation of one or more sets of digital media items thatsatisfy the first query. By way of non-limiting example, a first set1208 of digital media items (e.g., images) is shown within the firstdisplay element 1206. The first set 1208 may comprise digital mediaitems that satisfy the first query, “cat.” It is noted that such a querymay be likely to return results of a wide variety of digital media itemsassociated with context information having the term “cat.” By way ofnon-limiting example, digital media items comprising images may includeimages of one or more of house cats, wild cats, drawings of cartooncats, people holding cats, and/or other types of images.

The first display element 1206 may be configured to receive userselection of one or more digital media items within the first set 1208of digital media items to allow the user to refine the first query. Byway of non-limiting example, a user may select a first digital mediaitem 1210 and/or other digital media items (e.g., the selection beingshown by the dashed-line box around the digital media item 1210). Basedon the user selection, the first query may be updated (e.g., inaccordance with one or more of the selection component 114, querycomponent 108, and/or other components of machine-readable instruction106 shown in FIG. 1 and described herein). By way of non-limitingexample, the first query may be updated based on context informationassociated with the first digital media item 1210. As an illustrativeexample, the first digital media item 1210 may be associated withdescriptive context information including one or more of “person holdinganimal,” and/or other information. The first query may be updated toinclude terms such as “cat” and “person holding animal.”

FIG. 13 illustrates an exemplary implementation of the user interface1200 showing results from an updated search using the updated firstquery. Based on the updated first query, the first display element 1206may be configured to display a second set 1302 of digital media itemsthat satisfy the updated first query. Due to the further refinement ofthe first query based on the user selection, the second set 1302includes digital media items that satisfy both “cat” and “person boldinganimal” As shown, the second set 1302 may include different imagesportraying people holding cats, and/or other images. The second set 1302of digital media items may represent a user's desired search resultswhen they may not have been entirely certain of search terms to use inan initial search. It is noted that the first query and/or updated firstquery may be iteratively refined based on one or more user selections offurther digital media items shown in the display sets of digital mediaitems.

The second input element 1204 may be configured to receive user entry ofcontext information and/or other information. By way of non-limitingillustration, the user-entered context information is shows as“peron_holding_cat,” and/or other terms. The user-entered contextinformation may facilitate updating context information associated withindividual ones of the digital media items of the second set 1302. Assuch, the user-entered context information may be indexed for theindividual ones of the digital media items of the second set 1302 and/oravailable as a new searchable term for digital media items.

It is noted that the digital media items in the second set 1302 ofdigital media item shown in FIG. 13 may not all comprise images of aperson holding a cat. This may be due to errors during contextinformation prediction for digital media items. As such, in someimplementations, a user may continue to refine their search by selectingone or more digital media items in the second set 1302 such that one ormore other sets of digital media items may be searched for based onfurther updated queries.

In some implementations, a refined query may be given a name and savedfor future retrieval by using the name. For example, in the context of aconsumer photo organization application, the user may search for “dog”with a text query, and then select one or more images of their own dogfrom the retrieved results, and save the query with the name of theirdog “Molly” This may provide an online learning interface for trainingthe system in real-time about new categories that are either specific tothe given user (e.g., the name of their pet, “Molly”) or general to manyusers. The saved query name and the example images chosen by the usermay be stored by system 100. The saved query name may be aggregated overusers to form new training data for the machine learning system, therebygrowing the vocabulary of tags recognizable by the system.

Returning to FIG. 1, the server 102, external resources 122, digitalmedia item repository 124, and/or computing platforms 126 may beoperatively linked via one or more electronic communication links. Forexample, such electronic communication links may be established, atleast in part, via a communication network 120 such as the Internetand/or other networks. It will be appreciated that this is not intendedto be limiting, and that the scope of this disclosure includesimplementations in which server 102, external resources 122, digitalmedia item repository 124, and/or computing platforms 126 may beoperatively linked via some other communication media.

The external resources 122 may include sources of information, hostsand/or providers of information outside of system 100, external entitiesparticipating with system 100, and/or other resources. In someimplementations, some or all of the functionality attributed herein toexternal resources 122 may be provided by resources included in system100 (e.g., in server 102).

The server 102 may include electronic storage 118, one or moreprocessors 104, and/or other components. The server 102 may includecommunication lines or ports to enable the exchange of information witha network and/or other computing platforms 126. Illustration of server102 in FIG. 1 is not intended to be limiting. The server 102 may includea plurality of hardware, software, and/or firmware components operatingtogether to provide the functionality attributed herein to server 102.

Electronic storage 118 may comprise electronic storage media thatelectronically stores information. The electronic storage media ofelectronic storage 118 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server 102and/or removable storage that is removably connectable to server 102via, for example, a port or a drive. A port may include a USB pout, afirewire port, and/or other port. A drive may include a disk driveand/or other drive. Electronic storage 118 may include one or more ofoptically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e g, magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM. RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. The electronicstorage 118 may include one or more virtual storage resources (e.g.,cloud storage, a virtual private network, and/or other virtual storageresources). Electronic storage 118 may store software algorithms,information determined by processor 104, information received fromserver 102, and/or other information that enables server 102 to functionas described herein.

Processor(s) 104 may be configured to provide information processingcapabilities in server 102. As such, processor 104 may include one ormore of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor 104 is shown in FIG. 1 as asingle entity, this is for illustrative purposes only. In someimplementations, processor 104 may include one or more components. Thesecomponents may be physically located within the same device, orprocessor 104 may represent processing functionality of a plurality ofdevices operating in coordination. The processor 104 may be configuredto execute components 108, 110, 112, 114, and/or 116. Processor 104 maybe configured to execute components 108, 110, 112, 114, and/or 116 bysoftware, hardware; firmware; some combination of software, hardware,and/or firmware; and/or other mechanisms for configuring processingcapabilities on processor 104

It should be appreciated that, although components 108, 110, 112, 114,and/or 116 are illustrated in FIG. 1 as being co-located within a singlecomponent, in implementations in which processor 104 includes multiplecomponents, one or more of components 108, 110, 112, 114, and/or 116 maybe located remotely from the other components. The description of thefunctionality provided by the different components 108, 110, 112, 114,and/or 116 described above is for illustrative purposes and is notintended to be limiting, as any of components 108, 110, 112, 114, and/or116 may provide more or less functionality than is described. Forexample, one or more of components 108, 110, 112, 114, and/or 116 may beeliminated, and some or all of its functionality may be provided byother ones of components 106, 108, 110, 112, 114, 116, and/or othercomponents. As another example, processor 104 may be configured toexecute one or more additional components that may perform some or allof the functionality attributed below to one or more of components 108,110, 112, 114, and/or 116.

FIG. 3 illustrates a method 300 for facilitating searching, labeling,and/or filtering of digital media items, in accordance with one or moreimplementations. The operations of method 300 presented below areintended to be illustrative. In some embodiments, method 300 may beaccomplished with one or more additional operations not described and/orwithout one or more of the operations discussed. Additionally, the orderin which the operations of method 300 are illustrated in FIG. 3 anddescribed below is not intended to be limiting.

In some embodiments, method 300 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, a functionally limitedprocessing device, and/or other mechanisms for electronically processinginformation). The one or more processing devices may include one or moredevices executing some or all of the operations of method 300 inresponse to instructions stored electronically on an electronic storagemedium. The one or more processing devices may include one or moredevices configured through hardware, firmware, and/or software to bespecifically designed for execution of one or more of the operations ofmethod 300.

Referring now to method 300 in FIG. 3, at an operation 302, one or morequeries for digital media items may be obtained. By way of non-limitingexample, a first query and/or other queries may be obtained. In someimplementations, operation 302 may be performed by one or more physicalprocessors executing a query component the same as or similar to querycomponent 108 (shown in FIG. 1 and described herein).

At an operation 304, searches may be performed within one or moredigital media item repositories for one or more digital media items thatsatisfy the obtained queries. By way of non-limiting example, a firstsearch and/or other searches may be performed within a first digitalmedia item repository and/or other digital media item repositories forone or more digital media items that satisfy the first query and/orother queries. In some implementations, operation 304 may be performedby one or more physical processors executing a search component the sameas or similar to search component 110 (shown in FIG. 1 and describedherein).

At an operation 306, presentation of sets of digital media items thatsatisfy the obtained queries may be effectuated via computing platformsassociated with users. By way of non-limiting example, responsive to thefirst search returning a first set of digital media items that satisfythe first query, presentation of the first set of digital media itemsmay be effectuated on a first computing platform. In someimplementations, operation 306 may be performed by one or more physicalprocessors executing a results component the same as or similar toresults component 112 (shown in FIG. 1 and described herein).

At an operation 308, user selection of one or more digital media itemsin the presented sets of digital media items may be obtained. By way ofnon-limiting example, A first user selection may be obtained. The firstuser selection may comprise a selection by a first user via the firstcomputing platform of a first digital media item included in the firstset of digital media items. In some implementations, operation 308 maybe performed by one or more physical processors executing a selectioncomponent the same as or similar to selection component 114 (shown inFIG. 1 and described herein).

At an operation 310, one or more queries for digital media items may beupdated based on one or more user-selected digital media items. By wayof non-limiting example, the first query may be updated based on thefirst digital media item. In some implementations, operation 310 may beperformed by one or more physical processors executing a query componentthe same as or similar to query component 108 (shown in FIG. 1 anddescribed herein).

At an operation 312, searches may be performed within one or moredigital media item repositories for one or more digital media items thatsatisfy updated queries. By way of non-limiting example, a second searchand/or other searches may be performed within a first digital media itemrepository and/or other digital media item repositories for one or moredigital media items that satisfy the updated first query and/or otherupdated queries. In some implementations, operation 312 may be performedby one or more physical processors executing a search component the sameas or similar to search component 110 (shown in FIG. 1 and describedherein)

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementation, it is to be understood thatsuch detail is solely for that purpose and that the technology is notlimited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

1-20. (canceled)
 21. A computing system, comprising: a memory configuredto store instructions for displaying a user interface; and a processorconfigured to display, via the user interface, a first set of mediaitems, the first set of media items identified responsive to a searchrequest; receive, via the user interface, a selection of a selectedmedia item from the first set of media items; display, via the userinterface, a second set of media items, the second set of media itemsresponsive to an updated search request based on the selected mediaitem, the second set of media items grouped together based onsimilarities of the second set to one another; display, via the userinterface, a predicted name for the second set of media items, thepredicted name determined via a visual-semantic embedding space of amachine learning system based on information associated with the secondset of media items; and receive, via the user interface, auser-indicated update to the predicted name, the user-indicated updateto the predicted name provided to the machine learning system with theselected media item to cause the machine learning system to be trainedbased on the user-indicated update and the selected media item.
 22. Thecomputing system of claim 21, wherein the processor is furtherconfigured to, in the display of the first set of media items: identifya central media item of the first set of media items where the centralmedia item is in a central representative point in the visual-semanticembedding space associated with the first set of media items; anddisplay the central media item in a position of priority on the userinterface.
 23. The computing system of claim 22, wherein the processoris further configured to, in the display of the central media item:display the central media item in a center of a display area of the userinterface; and display the other media items in the first set of mediaitems around the central media item.
 24. The computing system of claim21, wherein the updated search request is further based on: contextinformation obtained via the machine learning system, the contextinformation associated with the selected media item, wherein at leastsome of the context information is predicted by the machine learningsystem.
 25. The computing system of claim 21, wherein the first set ofmedia items are mapped to points in the visual-semantic embedding space,wherein the processor is further configured to: Assign media items ofthe first set of media items to a first group based on similarities ofthe media items of the first set to one another; and cause the first setof media items to be presented on the user interface such that (i) acentral media item of the first group mapped to a most centralrepresentative point in the visual-semantic embedding space associatedwith the first group is presented in a position of priority, theposition of priority being proximate to a center of a display portion ofthe user interface and (ii) other media items of the first group arepresented around the central media item.
 26. The computing system ofclaim 25, wherein the processor is further configured to: present eachof the media items of the first group overlaid over one another in astack arrangement view; and receive, via the user interface, a signal tocause the stack arrangement view to be changed such that the centralmedia item of the first group is presented in the position of priorityand the other media items of the first group are presented around aperiphery of the central media item.
 27. The computing system of claim25, wherein the central media item of the first group is portrayedlarger relative to the other media items of the first group.
 28. Thecomputing system of claim 25, wherein the second set of media items aremapped to points in the visual-semantic embedding space, and wherein themedia items of a second group are presented on the user interface suchthat (i) a second central media item of the second group mapped to amost central representative point in the visual-semantic embedding spaceassociated with the second group is presented in the position ofpriority, and (ii) other media items of the second group are presentedaround the second central media item.
 29. A method, comprising:displaying, via a user interface, a first set of media items, the firstset of media items identified responsive to a search request; receiving,via the user interface, a selection of a selected media item from thefirst set of media items; displaying, via the user interface, a secondset of media items, the second set of media items responsive to anupdated search request based on the selected media item, the second setof media items grouped together based on similarities of the second setto one another; displaying, via the user interface, a predicted name forthe second set of media items, the predicted name determined via avisual-semantic embedding space of a machine learning system based oninformation associated with the second set of media items; andreceiving, via the user interface, a user-indicated update to thepredicted name, the user-indicated update to the predicted name providedto the machine learning system with the selected media item to cause themachine learning system to be trained based on the user-indicated updateand the selected media item.
 30. The method of claim 29, wherein thedisplay of the first set of media items further comprises: identifying acentral media item of the first set of media items where the centralmedia item is in a central representative point in the visual-semanticembedding space associated with the first set of media items; anddisplaying the central media item in a position of priority on the userinterface.
 31. The method of claim 30, wherein the display of thecentral media item further comprises: displaying the central media itemin a center of a display area of the user interface; and displaying theother media items in the first set of media items around the centralmedia item.
 32. The method of claim 29, wherein the updated searchrequest is further based on: context information obtained via themachine learning system, the context information associated with theselected media item, wherein at least some of the context information ispredicted by the machine learning system.
 33. The method of claim 29,wherein the first set of media items are mapped to points in thevisual-semantic embedding space, the method further comprising:assigning media items of the first set of media items to a first groupbased on similarities of the media items of the first set to oneanother; and causing the first set of media items to be presented on theuser interface such that (i) a central media item of the first groupmapped to a most central representative point in the visual-semanticembedding space associated with the first group is presented in aposition of priority, the position of priority being proximate to acenter of a display portion of the user interface and (ii) other mediaitems of the first group are presented around the central media item.34. A non-transitory computer-readable medium comprising instructionswhich when executed by a processor cause a computer to perform a methodcomprising: displaying, via a user interface, a first set of mediaitems, the first set of media items identified responsive to a searchrequest; receiving, via the user interface, a selection of a selectedmedia item from the first set of media items; displaying, via the userinterface, a second set of media items, the second set of media itemsresponsive to an updated search request based on the selected mediaitem, the second set of media items grouped together based onsimilarities of the second set to one another; displaying, via the userinterface, a predicted name for the second set of media items, thepredicted name determined via a visual-semantic embedding space of amachine learning system based on information associated with the secondset of media items; and receiving, via the user interface, auser-indicated update to the predicted name, the user-indicated updateto the predicted name provided to the machine learning system with theselected media item to cause the machine learning system to be trainedbased on the user-indicated update and the selected media item.